Overview

Brought to you by YData

Dataset statistics

Number of variables47
Number of observations69686
Missing cells410
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory251.4 MiB
Average record size in memory3.7 KiB

Variable types

Text19
Categorical17
Numeric6
Unsupported5

Alerts

apartado is highly overall correlated with etapa_acto_apertura and 2 other fieldsHigh correlation
año_apertura is highly overall correlated with periodo_aperturaHigh correlation
encuadre_legal is highly overall correlated with etapa_acto_apertura and 6 other fieldsHigh correlation
etapa is highly overall correlated with etapa_licHigh correlation
etapa_acto_apertura is highly overall correlated with apartado and 4 other fieldsHigh correlation
etapa_autorizacion_llamado is highly overall correlated with apartado and 4 other fieldsHigh correlation
etapa_autorizacion_pliego is highly overall correlated with apartado and 4 other fieldsHigh correlation
etapa_lic is highly overall correlated with etapaHigh correlation
financiamiento_externo is highly overall correlated with encuadre_legal and 1 other fieldsHigh correlation
genera_recursos is highly overall correlated with encuadre_legal and 2 other fieldsHigh correlation
modalidad is highly overall correlated with financiamiento_externo and 1 other fieldsHigh correlation
ofertas_confirmadas is highly overall correlated with proveedores_participantesHigh correlation
periodo_apertura is highly overall correlated with año_aperturaHigh correlation
proveedores_participantes is highly overall correlated with ofertas_confirmadasHigh correlation
tipo_doc_compra is highly overall correlated with encuadre_legal and 3 other fieldsHigh correlation
tipo_proceso is highly overall correlated with encuadre_legal and 5 other fieldsHigh correlation
etapa is highly imbalanced (99.2%) Imbalance
modalidad is highly imbalanced (81.3%) Imbalance
moneda is highly imbalanced (88.7%) Imbalance
tipo_doc_compra is highly imbalanced (88.1%) Imbalance
requiere_pago is highly imbalanced (98.3%) Imbalance
apartado is highly imbalanced (56.7%) Imbalance
etapa_lic is highly imbalanced (99.2%) Imbalance
genera_recursos is highly imbalanced (92.6%) Imbalance
financiamiento_externo is highly imbalanced (97.3%) Imbalance
num_proceso has unique values Unique
año_publicacion is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_publicacion is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_inicio_consultas is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_final_consultas is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_acto_apertura is an unsupported type, check if it needs cleaning or further analysis Unsupported
cro_cant_dias_publicar has 60983 (87.5%) zeros Zeros
proveedores_participantes has 3353 (4.8%) zeros Zeros
ofertas_confirmadas has 6210 (8.9%) zeros Zeros

Reproduction

Analysis started2025-05-27 21:51:08.246598
Analysis finished2025-05-27 21:52:02.405598
Duration54.16 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

num_proceso
Text

Unique 

Distinct69686
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
2025-05-27T18:52:03.257008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length14.628907
Min length13

Characters and Unicode

Total characters1019430
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69686 ?
Unique (%)100.0%

Sample

1st row23-0009-LPR16
2nd row23-0010-LPR16
3rd row23-0011-LPR16
4th row23-0012-LPR16
5th row23-0014-LPU16
ValueCountFrequency (%)
23-0017-lpu16 1
 
< 0.1%
98-0027-cdi22 1
 
< 0.1%
23-0009-lpr16 1
 
< 0.1%
23-0010-lpr16 1
 
< 0.1%
23-0011-lpr16 1
 
< 0.1%
23-0012-lpr16 1
 
< 0.1%
87-0001-cdi22 1
 
< 0.1%
87-0054-cdi22 1
 
< 0.1%
87-0056-cdi22 1
 
< 0.1%
87-0075-cdi22 1
 
< 0.1%
Other values (69676) 69676
> 99.9%
2025-05-27T18:52:04.570320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1019430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1019430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1019430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%
Distinct66201
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-05-27T18:52:05.486507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length41
Mean length33.60599
Min length29

Characters and Unicode

Total characters2341867
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62977 ?
Unique (%)90.4%

Sample

1st rowEX-2016-00697885- -APN-DPYS#SGP
2nd rowEX-2016-01031683- -APN-DPYS#SGP
3rd rowEX-2016-01358346- -APN-DDMYA#SGP
4th rowEX-2016-01392005- -APN-DDMYA#SGP
5th rowEX-2016-00474707- -APN-DPYS#SGP
ValueCountFrequency (%)
apn-dcon#faa 1991
 
1.4%
apn-dgit#ara 1423
 
1.0%
apn-dcyc#mc 1095
 
0.8%
apn-dacmysg#anlis 1004
 
0.7%
apn-dc#hp 946
 
0.7%
apn-dcyc#mds 827
 
0.6%
apn-gaen#cnea 800
 
0.6%
apn-da#inidep 791
 
0.6%
apn-dmza#dnv 787
 
0.6%
apn-gasnya#cnea 776
 
0.6%
Other values (66773) 128932
92.5%
2025-05-27T18:52:06.707815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%
Distinct60794
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
2025-05-27T18:52:07.887984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length253
Median length133
Mean length62.627343
Min length5

Characters and Unicode

Total characters4364249
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56308 ?
Unique (%)80.8%

Sample

1st rowAdquisición de elementos de electricidad
2nd rowAdquisición de elementos de plomería y cerrajería.
3rd rowADQUISICIÓN INSUMOS PARA BAÑOS
4th rowServicio anual de mantenimiento, y controles mensuales de Extintores, y adquisición
5th rowAdquisición de indumentaria.
ValueCountFrequency (%)
de 109302
 
17.0%
para 27722
 
4.3%
y 25932
 
4.0%
adquisición 24358
 
3.8%
servicio 16245
 
2.5%
la 12477
 
1.9%
del 11279
 
1.8%
el 10265
 
1.6%
7475
 
1.2%
mantenimiento 6952
 
1.1%
Other values (25911) 389171
60.7%
2025-05-27T18:52:09.906659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4364249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4364249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4364249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

tipo_proceso
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
Contratación Directa
37296 
Licitación Privada
22944 
Licitación Pública
8530 
Subasta Pública
 
395
Concurso Privado
 
274
Other values (2)
 
247

Length

Max length20
Median length20
Mean length19.039649
Min length15

Characters and Unicode

Total characters1326797
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLicitación Privada
2nd rowLicitación Privada
3rd rowLicitación Privada
4th rowLicitación Privada
5th rowLicitación Pública

Common Values

ValueCountFrequency (%)
Contratación Directa 37296
53.5%
Licitación Privada 22944
32.9%
Licitación Pública 8530
 
12.2%
Subasta Pública 395
 
0.6%
Concurso Privado 274
 
0.4%
Concurso Público 219
 
0.3%
Compulsa de Precios 28
 
< 0.1%

Length

2025-05-27T18:52:10.315619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:10.891883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
contratación 37296
26.8%
directa 37296
26.8%
licitación 31474
22.6%
privada 22944
16.5%
pública 8925
 
6.4%
concurso 493
 
0.4%
subasta 395
 
0.3%
privado 274
 
0.2%
público 219
 
0.2%
compulsa 28
 
< 0.1%
Other values (2) 56
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1326797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1326797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1326797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%
Distinct15397
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2025-05-27T18:52:11.839916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1463406
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5611 ?
Unique (%)8.1%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:30 Hrs.
4th row30/11/2016 16:31 Hrs.
5th row27/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69686
33.3%
10:00 18690
 
8.9%
12:00 11030
 
5.3%
11:00 10445
 
5.0%
09:00 8590
 
4.1%
08:00 3907
 
1.9%
13:00 3816
 
1.8%
15:00 2640
 
1.3%
16:00 1855
 
0.9%
14:00 1583
 
0.8%
Other values (1666) 76816
36.7%
2025-05-27T18:52:13.042295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1463406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1463406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1463406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

estado
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
Adjudicado
54562 
Dejado Sin Efecto
13428 
Desierto
 
1696

Length

Max length17
Median length10
Mean length11.300175
Min length8

Characters and Unicode

Total characters787464
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdjudicado
2nd rowDesierto
3rd rowAdjudicado
4th rowAdjudicado
5th rowAdjudicado

Common Values

ValueCountFrequency (%)
Adjudicado 54562
78.3%
Dejado Sin Efecto 13428
 
19.3%
Desierto 1696
 
2.4%

Length

2025-05-27T18:52:13.446866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:13.864170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
adjudicado 54562
56.5%
dejado 13428
 
13.9%
sin 13428
 
13.9%
efecto 13428
 
13.9%
desierto 1696
 
1.8%

Most occurring characters

ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 787464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 787464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 787464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%
Distinct482
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-05-27T18:52:14.966373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length149
Median length84
Mean length44.907169
Min length13

Characters and Unicode

Total characters3129401
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row23/000 - Dirección General de Administración - SG
2nd row23/000 - Dirección General de Administración - SG
3rd row23/000 - Dirección General de Administración - SG
4th row23/000 - Dirección General de Administración - SG
5th row23/000 - Dirección General de Administración - SG
ValueCountFrequency (%)
104265
 
20.0%
de 47301
 
9.1%
dirección 20429
 
3.9%
general 17242
 
3.3%
y 15439
 
3.0%
administración 13784
 
2.6%
compras 12303
 
2.4%
contrataciones 10812
 
2.1%
dnv 8451
 
1.6%
departamento 5389
 
1.0%
Other values (1182) 266717
51.1%
2025-05-27T18:52:16.508755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3129401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3129401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3129401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%
Distinct144
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.1 MiB
2025-05-27T18:52:17.712059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length102
Median length84
Mean length43.07287
Min length25

Characters and Unicode

Total characters3001576
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row301 - Secretaria General de la Presidencia de la Nación
2nd row301 - Secretaria General de la Presidencia de la Nación
3rd row301 - Secretaria General de la Presidencia de la Nación
4th row301 - Secretaria General de la Presidencia de la Nación
5th row301 - Secretaria General de la Presidencia de la Nación
ValueCountFrequency (%)
69731
 
13.7%
de 58890
 
11.6%
nacional 28106
 
5.5%
general 22383
 
4.4%
estado 21663
 
4.3%
mayor 21284
 
4.2%
la 16595
 
3.3%
del 11499
 
2.3%
dirección 9519
 
1.9%
ejercito 8823
 
1.7%
Other values (387) 239352
47.1%
2025-05-27T18:52:19.213284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3001576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3001576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3001576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Unnamed: 0.1
Real number (ℝ)

Distinct1352
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.35604
Minimum0
Maximum1351
Zeros237
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T18:52:19.512366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q198
median237
Q3534
95-th percentile983
Maximum1351
Range1351
Interquartile range (IQR)436

Descriptive statistics

Standard deviation309.45645
Coefficient of variation (CV)0.89865259
Kurtosis0.047743009
Mean344.35604
Median Absolute Deviation (MAD)173
Skewness1.0031102
Sum23996795
Variance95763.293
MonotonicityNot monotonic
2025-05-27T18:52:19.847155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
 
0.3%
1 237
 
0.3%
2 236
 
0.3%
3 236
 
0.3%
4 235
 
0.3%
5 234
 
0.3%
6 234
 
0.3%
7 232
 
0.3%
8 230
 
0.3%
9 229
 
0.3%
Other values (1342) 67346
96.6%
ValueCountFrequency (%)
0 237
0.3%
1 237
0.3%
2 236
0.3%
3 236
0.3%
4 235
0.3%
5 234
0.3%
6 234
0.3%
7 232
0.3%
8 230
0.3%
9 229
0.3%
ValueCountFrequency (%)
1351 1
< 0.1%
1350 1
< 0.1%
1349 1
< 0.1%
1348 1
< 0.1%
1347 1
< 0.1%
1346 1
< 0.1%
1345 1
< 0.1%
1344 1
< 0.1%
1343 1
< 0.1%
1342 1
< 0.1%
Distinct66201
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-05-27T18:52:21.298136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length41
Mean length33.60599
Min length29

Characters and Unicode

Total characters2341867
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62977 ?
Unique (%)90.4%

Sample

1st rowEX-2016-00697885- -APN-DPYS#SGP
2nd rowEX-2016-01031683- -APN-DPYS#SGP
3rd rowEX-2016-01358346- -APN-DDMYA#SGP
4th rowEX-2016-01392005- -APN-DDMYA#SGP
5th rowEX-2016-00474707- -APN-DPYS#SGP
ValueCountFrequency (%)
apn-dcon#faa 1991
 
1.4%
apn-dgit#ara 1423
 
1.0%
apn-dcyc#mc 1095
 
0.8%
apn-dacmysg#anlis 1004
 
0.7%
apn-dc#hp 946
 
0.7%
apn-dcyc#mds 827
 
0.6%
apn-gaen#cnea 800
 
0.6%
apn-da#inidep 791
 
0.6%
apn-dmza#dnv 787
 
0.6%
apn-gasnya#cnea 776
 
0.6%
Other values (66773) 128932
92.5%
2025-05-27T18:52:22.800834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

etapa
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Única
69612 
Múltiple
 
45
Otros
 
29

Length

Max length8
Median length5
Mean length5.0019373
Min length5

Characters and Unicode

Total characters348565
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÚnica
2nd rowÚnica
3rd rowÚnica
4th rowÚnica
5th rowÚnica

Common Values

ValueCountFrequency (%)
Única 69612
99.9%
Múltiple 45
 
0.1%
Otros 29
 
< 0.1%

Length

2025-05-27T18:52:23.195520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:23.531862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
única 69612
99.9%
múltiple 45
 
0.1%
otros 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

modalidad
Categorical

High correlation  Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Sin modalidad
58398 
Orden de compra abierta
10228 
Sin modalidad - Llave en mano
 
840
Selección Directa
 
107
Solicitud De Cotizaciones
 
27
Other values (10)
 
86

Length

Max length60
Median length13
Mean length14.690081
Min length13

Characters and Unicode

Total characters1023693
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSin modalidad
2nd rowSin modalidad
3rd rowOrden de compra abierta
4th rowSin modalidad
5th rowSin modalidad

Common Values

ValueCountFrequency (%)
Sin modalidad 58398
83.8%
Orden de compra abierta 10228
 
14.7%
Sin modalidad - Llave en mano 840
 
1.2%
Selección Directa 107
 
0.2%
Solicitud De Cotizaciones 27
 
< 0.1%
Solicitud De Ofertas 23
 
< 0.1%
Orden de compra abierta - Llave en mano 17
 
< 0.1%
Acuerdo Marco 15
 
< 0.1%
Sin modalidad - Precio máximo 9
 
< 0.1%
Sin modalidad - Compra Consolidada 8
 
< 0.1%
Other values (5) 14
 
< 0.1%

Length

2025-05-27T18:52:23.943302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sin 59263
36.3%
modalidad 59263
36.3%
de 10302
 
6.3%
compra 10260
 
6.3%
orden 10247
 
6.3%
abierta 10247
 
6.3%
889
 
0.5%
llave 858
 
0.5%
en 858
 
0.5%
mano 858
 
0.5%
Other values (15) 406
 
0.2%

Most occurring characters

ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

moneda
Categorical

Imbalance 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Peso Argentino
65023 
Dolar Estadounidense
 
2150
Dolar Estadounidense Peso Argentino
 
1735
Dolar Estadounidense Euro - European Monetary Union Peso Argentino
 
483
Euro - European Monetary Union
 
141
Other values (13)
 
154

Length

Max length87
Median length14
Mean length15.166662
Min length4

Characters and Unicode

Total characters1056904
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPeso Argentino
2nd rowPeso Argentino
3rd rowPeso Argentino
4th rowPeso Argentino
5th rowPeso Argentino

Common Values

ValueCountFrequency (%)
Peso Argentino 65023
93.3%
Dolar Estadounidense 2150
 
3.1%
Dolar Estadounidense Peso Argentino 1735
 
2.5%
Dolar Estadounidense Euro - European Monetary Union Peso Argentino 483
 
0.7%
Euro - European Monetary Union 141
 
0.2%
Dolar Estadounidense Euro - European Monetary Union 100
 
0.1%
Euro - European Monetary Union Peso Argentino 20
 
< 0.1%
sin datos 16
 
< 0.1%
Libra Esterlina 6
 
< 0.1%
Dolar Estadounidense Real 2
 
< 0.1%
Other values (8) 10
 
< 0.1%

Length

2025-05-27T18:52:24.310529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peso 67267
45.7%
argentino 67267
45.7%
dolar 4476
 
3.0%
estadounidense 4476
 
3.0%
euro 749
 
0.5%
749
 
0.5%
european 749
 
0.5%
monetary 749
 
0.5%
union 749
 
0.5%
sin 16
 
< 0.1%
Other values (4) 46
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1056904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1056904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1056904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

encuadre_legal
Categorical

High correlation 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14
31150 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.12
17560 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.10
6243 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.12
3919 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14
3321 
Other values (25)
7493 

Length

Max length144
Median length64
Mean length65.877264
Min length25

Characters and Unicode

Total characters4590723
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowDecreto Delegado N° 1023/2001 Art. 25
2nd rowDecreto Delegado N° 1023/2001 Art. 25
3rd rowDecreto Delegado N° 1023/2001 Art. 25
4th rowDecreto Delegado N° 1023/2001 Art. 25
5th rowDecreto Delegado N° 1023/2001 Art. 25

Common Values

ValueCountFrequency (%)
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14 31150
44.7%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.12 17560
25.2%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.10 6243
 
9.0%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.12 3919
 
5.6%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14 3321
 
4.8%
Decreto Delegado N° 1023/2001 Art. 25 2282
 
3.3%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.10 Decreto N°1030/2016 Art. 25 1892
 
2.7%
Decreto N°1030/2016 Art.12 783
 
1.1%
Decreto N°1030/2016 Art.14 506
 
0.7%
SUPERINTENDENCIA DE BIENESTAR Decreto 910/2018 415
 
0.6%
Other values (20) 1615
 
2.3%

Length

2025-05-27T18:52:24.718653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
decreto 143019
22.1%
art 76967
11.9%
25 76505
11.8%
n°1030/2016 75332
11.6%
67147
10.4%
delegado 66878
10.3%
1023/2001 66878
10.3%
art.14 35114
 
5.4%
art.12 22340
 
3.5%
art.10 8251
 
1.3%
Other values (56) 8875
 
1.4%

Most occurring characters

ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

cotizacion
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
No admite cotización parcial por renglón
57499 
Se admite cotización parcial por renglón
12187 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters2787440
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo admite cotización parcial por renglón
2nd rowSe admite cotización parcial por renglón
3rd rowNo admite cotización parcial por renglón
4th rowNo admite cotización parcial por renglón
5th rowSe admite cotización parcial por renglón

Common Values

ValueCountFrequency (%)
No admite cotización parcial por renglón 57499
82.5%
Se admite cotización parcial por renglón 12187
 
17.5%

Length

2025-05-27T18:52:25.183705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:25.503576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
admite 69686
16.7%
cotización 69686
16.7%
parcial 69686
16.7%
renglón 69686
16.7%
por 69686
16.7%
no 57499
13.8%
se 12187
 
2.9%

Most occurring characters

ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

tipo_doc_compra
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Orden de compra
67341 
Contrato
 
1898
Orden de venta
 
432
Acuerdo
 
15

Length

Max length15
Median length15
Mean length14.801424
Min length7

Characters and Unicode

Total characters1031452
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrden de compra
2nd rowOrden de compra
3rd rowOrden de compra
4th rowOrden de compra
5th rowOrden de compra

Common Values

ValueCountFrequency (%)
Orden de compra 67341
96.6%
Contrato 1898
 
2.7%
Orden de venta 432
 
0.6%
Acuerdo 15
 
< 0.1%

Length

2025-05-27T18:52:25.963731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:26.318177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
orden 67773
33.0%
de 67773
33.0%
compra 67341
32.8%
contrato 1898
 
0.9%
venta 432
 
0.2%
acuerdo 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%
Distinct8245
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
2025-05-27T18:52:27.277421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length238
Mean length42.872528
Min length1

Characters and Unicode

Total characters2987615
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6314 ?
Unique (%)9.1%

Sample

1st row25 de Mayo 658, 4° piso.
2nd row25 de Mayo 658, 4° piso.
3rd row25 de Mayo 658, 4° piso.
4th row25 de Mayo 658, 4° piso.
5th row25 de Mayo 658, 4° piso.
ValueCountFrequency (%)
31108
 
5.6%
de 25265
 
4.6%
av 22886
 
4.1%
piso 19224
 
3.5%
caba 10440
 
1.9%
aires 8894
 
1.6%
buenos 8718
 
1.6%
y 8068
 
1.5%
7160
 
1.3%
oficina 6994
 
1.3%
Other values (4991) 403589
73.1%
2025-05-27T18:52:28.730558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2987615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2987615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2987615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%
Distinct69
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-05-27T18:52:29.395560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length33
Mean length32.989137
Min length23

Characters and Unicode

Total characters2298881
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row60 Días corridos Acto de apertura
2nd row60 Días corridos Acto de apertura
3rd row60 Días corridos Acto de apertura
4th row60 Días corridos Perfeccionamiento del documento contractual
5th row60 Días corridos Acto de apertura
ValueCountFrequency (%)
de 69657
16.7%
días 69566
16.6%
apertura 69505
16.6%
acto 69505
16.6%
60 66064
15.8%
corridos 64534
15.4%
hábiles 5032
 
1.2%
90 1333
 
0.3%
30 935
 
0.2%
120 559
 
0.1%
Other values (40) 1563
 
0.4%
2025-05-27T18:52:30.455901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

requiere_pago
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
No
69575 
 
111

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 69575
99.8%
111
 
0.2%

Length

2025-05-27T18:52:30.805881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:31.039136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 69575
99.8%
111
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

apartado
Categorical

High correlation  Imbalance 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
sin datos
32390 
Apartado 1: Compulsa Abreviada Por Monto
25203 
Apartado 3: Adjudicación Simple por Exclusividad
3461 
Apartado 8: Adjudicación Simple Interadministrativa
3256 
Apartado 2: Adjudicación Simple por Especialidad
 
1170
Other values (19)
4206 

Length

Max length84
Median length83
Mean length27.871265
Min length9

Characters and Unicode

Total characters1942237
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos

Common Values

ValueCountFrequency (%)
sin datos 32390
46.5%
Apartado 1: Compulsa Abreviada Por Monto 25203
36.2%
Apartado 3: Adjudicación Simple por Exclusividad 3461
 
5.0%
Apartado 8: Adjudicación Simple Interadministrativa 3256
 
4.7%
Apartado 2: Adjudicación Simple por Especialidad 1170
 
1.7%
Apartado 7: Adjudicación Simple por Desarme, Traslado o Examen Previo 1018
 
1.5%
Apartado 5: Compulsa Abreviada por Urgencia 764
 
1.1%
Apartado 9: Adjudicación Simple con Universidades Nacionales 656
 
0.9%
Apartado 11: Adjudicación Simple por Locación de Inmuebles 620
 
0.9%
Apartado 10: Adjudicación Simple con Efectores de Desarrollo Local y Economía Social 495
 
0.7%
Other values (14) 653
 
0.9%

Length

2025-05-27T18:52:31.411776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartado 37296
12.6%
por 32842
11.1%
datos 32390
11.0%
sin 32390
11.0%
abreviada 26422
9.0%
compulsa 26422
9.0%
monto 25302
8.6%
1 25205
8.5%
adjudicación 10853
 
3.7%
simple 10853
 
3.7%
Other values (52) 35237
11.9%

Most occurring characters

ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1942237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1942237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1942237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

etapa_lic
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Única
69612 
Múltiple
 
45
Otros
 
29

Length

Max length8
Median length5
Mean length5.0019373
Min length5

Characters and Unicode

Total characters348565
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÚnica
2nd rowÚnica
3rd rowÚnica
4th rowÚnica
5th rowÚnica

Common Values

ValueCountFrequency (%)
Única 69612
99.9%
Múltiple 45
 
0.1%
Otros 29
 
< 0.1%

Length

2025-05-27T18:52:31.840952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:32.251286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
única 69612
99.9%
múltiple 45
 
0.1%
otros 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

etapa_autorizacion_pliego
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
sin datos
42824 
Autorización del pliego
26862 

Length

Max length23
Median length9
Mean length14.396608
Min length9

Characters and Unicode

Total characters1003242
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos

Common Values

ValueCountFrequency (%)
sin datos 42824
61.5%
Autorización del pliego 26862
38.5%

Length

2025-05-27T18:52:32.533964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:32.803456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sin 42824
25.8%
datos 42824
25.8%
autorización 26862
16.2%
del 26862
16.2%
pliego 26862
16.2%

Most occurring characters

ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1003242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1003242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1003242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

etapa_autorizacion_llamado
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
sin datos
42878 
Autorización de llamado
26808 

Length

Max length23
Median length9
Mean length14.385759
Min length9

Characters and Unicode

Total characters1002486
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos

Common Values

ValueCountFrequency (%)
sin datos 42878
61.5%
Autorización de llamado 26808
38.5%

Length

2025-05-27T18:52:33.160126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:33.457889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sin 42878
25.8%
datos 42878
25.8%
autorización 26808
16.1%
de 26808
16.1%
llamado 26808
16.1%

Most occurring characters

ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

etapa_acto_apertura
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
Acto de apertura
36406 
sin datos
33280 

Length

Max length16
Median length16
Mean length12.657004
Min length9

Characters and Unicode

Total characters882016
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos

Common Values

ValueCountFrequency (%)
Acto de apertura 36406
52.2%
sin datos 33280
47.8%

Length

2025-05-27T18:52:33.819252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:34.158306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
acto 36406
20.7%
de 36406
20.7%
apertura 36406
20.7%
sin 33280
18.9%
datos 33280
18.9%

Most occurring characters

ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

genera_recursos
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
No
69055 
Si
 
631

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 69055
99.1%
Si 631
 
0.9%

Length

2025-05-27T18:52:34.484310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:34.785417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 69055
99.1%
si 631
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

financiamiento_externo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
No
69500 
Si
 
186

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 69500
99.7%
Si 186
 
0.3%

Length

2025-05-27T18:52:35.099134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:35.388892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 69500
99.7%
si 186
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

acepta_prorroga
Categorical

Distinct2
Distinct (%)< 0.1%
Missing410
Missing (%)0.6%
Memory size4.4 MiB
No
50932 
18344 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters138552
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 50932
73.1%
18344
 
26.3%
(Missing) 410
 
0.6%

Length

2025-05-27T18:52:35.697407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T18:52:35.979709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 50932
73.5%
18344
 
26.5%

Most occurring characters

ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%
Distinct25992
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2025-05-27T18:52:36.895888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.987429
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13788 ?
Unique (%)19.8%

Sample

1st row21/09/2016 18:01 Hrs.
2nd row02/09/2016 09:00 Hrs.
3rd row07/12/2016 18:30 Hrs.
4th row15/11/2016 16:30 Hrs.
5th row21/09/2016 15:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
08:00 13835
 
6.6%
10:00 8170
 
3.9%
09:00 6357
 
3.0%
12:00 4536
 
2.2%
14:00 3518
 
1.7%
15:00 3119
 
1.5%
13:00 3043
 
1.5%
11:00 2776
 
1.3%
16:00 2401
 
1.1%
Other values (2008) 91617
43.8%
2025-05-27T18:52:38.107787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%
Distinct31333
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2025-05-27T18:52:38.847740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.987429
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19336 ?
Unique (%)27.7%

Sample

1st row21/09/2016 18:02 Hrs.
2nd row02/09/2016 10:00 Hrs.
3rd row07/12/2016 19:00 Hrs.
4th row15/11/2016 16:31 Hrs.
5th row21/09/2016 15:01 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
10:00 9076
 
4.3%
08:00 8662
 
4.1%
09:00 8095
 
3.9%
11:00 4286
 
2.1%
12:00 2796
 
1.3%
08:30 1837
 
0.9%
08:01 1819
 
0.9%
13:00 1811
 
0.9%
15:00 1774
 
0.8%
Other values (2101) 99216
47.5%
2025-05-27T18:52:39.966152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%
Distinct19810
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2025-05-27T18:52:41.025326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.987429
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7106 ?
Unique (%)10.2%

Sample

1st row06/10/2016 13:01 Hrs.
2nd row07/09/2016 11:00 Hrs.
3rd row19/12/2016 12:00 Hrs.
4th row24/11/2016 16:31 Hrs.
5th row21/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
12:00 11635
 
5.6%
10:00 10560
 
5.1%
13:00 6186
 
3.0%
16:00 6012
 
2.9%
09:00 4407
 
2.1%
11:00 4370
 
2.1%
17:00 4140
 
2.0%
15:00 3592
 
1.7%
18:00 3590
 
1.7%
Other values (1867) 84880
40.6%
2025-05-27T18:52:42.085365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

cro_cant_dias_publicar
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25358035
Minimum0
Maximum30
Zeros60983
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T18:52:42.469282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72170485
Coefficient of variation (CV)2.8460599
Kurtosis104.73079
Mean0.25358035
Median Absolute Deviation (MAD)0
Skewness5.495288
Sum17671
Variance0.52085788
MonotonicityNot monotonic
2025-05-27T18:52:42.816817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 60983
87.5%
2 8401
 
12.1%
1 211
 
0.3%
3 24
 
< 0.1%
5 22
 
< 0.1%
10 16
 
< 0.1%
8 9
 
< 0.1%
7 8
 
< 0.1%
6 2
 
< 0.1%
20 2
 
< 0.1%
Other values (8) 8
 
< 0.1%
ValueCountFrequency (%)
0 60983
87.5%
1 211
 
0.3%
2 8401
 
12.1%
3 24
 
< 0.1%
4 1
 
< 0.1%
5 22
 
< 0.1%
6 2
 
< 0.1%
7 8
 
< 0.1%
8 9
 
< 0.1%
10 16
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 2
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
10 16
< 0.1%
8 9
< 0.1%
Distinct13987
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
2025-05-27T18:52:43.785667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length9
Mean length14.182217
Min length9

Characters and Unicode

Total characters988302
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8245 ?
Unique (%)11.8%

Sample

1st row27/09/2016 18:02 Hrs.
2nd row02/09/2016 09:00 Hrs.
3rd row12/12/2016 10:00 Hrs.
4th row30/11/2016 16:30 Hrs.
5th row26/09/2016 15:01 Hrs.
ValueCountFrequency (%)
sin 39592
23.4%
datos 39592
23.4%
hrs 30094
17.8%
10:00 5724
 
3.4%
08:00 5569
 
3.3%
09:00 3753
 
2.2%
11:00 2574
 
1.5%
12:00 1735
 
1.0%
07:00 942
 
0.6%
08:30 744
 
0.4%
Other values (1893) 39147
23.1%
2025-05-27T18:52:45.201570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%
Distinct13763
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
2025-05-27T18:52:46.501221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length9
Mean length14.182217
Min length9

Characters and Unicode

Total characters988302
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7541 ?
Unique (%)10.8%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:00 Hrs.
4th row30/11/2016 16:30 Hrs.
5th row27/10/2016 11:59 Hrs.
ValueCountFrequency (%)
sin 39592
23.4%
datos 39592
23.4%
hrs 30094
17.8%
12:00 5482
 
3.2%
10:00 4924
 
2.9%
11:00 4252
 
2.5%
09:00 2882
 
1.7%
08:00 1645
 
1.0%
13:00 1519
 
0.9%
16:00 1012
 
0.6%
Other values (1696) 38472
22.7%
2025-05-27T18:52:48.047793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%
Distinct15392
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2025-05-27T18:52:48.876260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.987429
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5610 ?
Unique (%)8.1%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:30 Hrs.
4th row30/11/2016 16:31 Hrs.
5th row27/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
10:00 18670
 
8.9%
12:00 11014
 
5.3%
11:00 10436
 
5.0%
09:00 8586
 
4.1%
08:00 3906
 
1.9%
13:00 3804
 
1.8%
15:00 2637
 
1.3%
16:00 1855
 
0.9%
14:00 1582
 
0.8%
Other values (1668) 76882
36.8%
2025-05-27T18:52:50.041657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%
Distinct230
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2025-05-27T18:52:50.840111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length88
Median length56
Mean length62.564934
Min length9

Characters and Unicode

Total characters4359900
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.1%

Sample

1st rowA partir del perfeccionamiento del documento contractual
2nd rowDentro de los 20 Días corridos del perfeccionamiento del documento contractual
3rd rowA partir del perfeccionamiento del documento contractual
4th rowA partir del perfeccionamiento del documento contractual
5th rowA partir del perfeccionamiento del documento contractual
ValueCountFrequency (%)
del 140364
24.2%
perfeccionamiento 69668
12.0%
contractual 69668
12.0%
documento 69668
12.0%
a 54447
 
9.4%
partir 45130
 
7.8%
los 24538
 
4.2%
días 24538
 
4.2%
de 15221
 
2.6%
dentro 15221
 
2.6%
Other values (110) 51168
 
8.8%
2025-05-27T18:52:51.848414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4359900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4359900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4359900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%
Distinct328
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2025-05-27T18:52:52.412608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.019172
Min length5

Characters and Unicode

Total characters837568
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.1%

Sample

1st row5 Días hábiles
2nd row30 Días corridos
3rd row12 Meses
4th row12 Meses
5th row120 Días corridos
ValueCountFrequency (%)
días 38418
21.6%
meses 29590
16.6%
corridos 19659
11.0%
hábiles 18759
10.5%
12 18310
10.3%
15 9256
 
5.2%
30 7963
 
4.5%
6 4404
 
2.5%
10 4089
 
2.3%
60 3661
 
2.1%
Other values (195) 24049
13.5%
2025-05-27T18:52:53.298250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 837568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 837568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 837568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

proveedores_participantes
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9332578
Minimum0
Maximum319
Zeros3353
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T18:52:53.510771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum319
Range319
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2377891
Coefficient of variation (CV)1.0617303
Kurtosis200.34176
Mean4.9332578
Median Absolute Deviation (MAD)2
Skewness5.456081
Sum343779
Variance27.434435
MonotonicityNot monotonic
2025-05-27T18:52:54.411783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17559
25.2%
2 8052
11.6%
3 6995
 
10.0%
4 5894
 
8.5%
5 4993
 
7.2%
6 4245
 
6.1%
7 3391
 
4.9%
0 3353
 
4.8%
8 2845
 
4.1%
9 2308
 
3.3%
Other values (53) 10051
14.4%
ValueCountFrequency (%)
0 3353
 
4.8%
1 17559
25.2%
2 8052
11.6%
3 6995
 
10.0%
4 5894
 
8.5%
5 4993
 
7.2%
6 4245
 
6.1%
7 3391
 
4.9%
8 2845
 
4.1%
9 2308
 
3.3%
ValueCountFrequency (%)
319 1
< 0.1%
139 1
< 0.1%
99 1
< 0.1%
74 1
< 0.1%
65 2
< 0.1%
64 1
< 0.1%
60 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%

ofertas_confirmadas
Real number (ℝ)

High correlation  Zeros 

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3316735
Minimum0
Maximum105
Zeros6210
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T18:52:54.740105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile10
Maximum105
Range105
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5503553
Coefficient of variation (CV)1.0656372
Kurtosis21.291565
Mean3.3316735
Median Absolute Deviation (MAD)1
Skewness2.7741991
Sum232171
Variance12.605023
MonotonicityNot monotonic
2025-05-27T18:52:55.031146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 22248
31.9%
2 10032
14.4%
3 7694
 
11.0%
0 6210
 
8.9%
4 5764
 
8.3%
5 4434
 
6.4%
6 3336
 
4.8%
7 2540
 
3.6%
8 1833
 
2.6%
9 1312
 
1.9%
Other values (34) 4283
 
6.1%
ValueCountFrequency (%)
0 6210
 
8.9%
1 22248
31.9%
2 10032
14.4%
3 7694
 
11.0%
4 5764
 
8.3%
5 4434
 
6.4%
6 3336
 
4.8%
7 2540
 
3.6%
8 1833
 
2.6%
9 1312
 
1.9%
ValueCountFrequency (%)
105 1
 
< 0.1%
78 1
 
< 0.1%
53 1
 
< 0.1%
45 1
 
< 0.1%
42 3
< 0.1%
38 3
< 0.1%
37 1
 
< 0.1%
36 3
< 0.1%
35 4
< 0.1%
34 3
< 0.1%

año_publicacion
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.2 MiB

año_apertura
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.0042
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T18:52:55.396527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2017
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5233129
Coefficient of variation (CV)0.00075411372
Kurtosis-0.92361892
Mean2020.0042
Median Absolute Deviation (MAD)1
Skewness-0.3188608
Sum1.4076601 × 108
Variance2.3204821
MonotonicityIncreasing
2025-05-27T18:52:55.557499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 16171
23.2%
2022 14210
20.4%
2019 13381
19.2%
2020 12628
18.1%
2018 9212
13.2%
2017 3840
 
5.5%
2016 244
 
0.4%
ValueCountFrequency (%)
2016 244
 
0.4%
2017 3840
 
5.5%
2018 9212
13.2%
2019 13381
19.2%
2020 12628
18.1%
2021 16171
23.2%
2022 14210
20.4%
ValueCountFrequency (%)
2022 14210
20.4%
2021 16171
23.2%
2020 12628
18.1%
2019 13381
19.2%
2018 9212
13.2%
2017 3840
 
5.5%
2016 244
 
0.4%

periodo_apertura
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202007.57
Minimum201608
Maximum202212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T18:52:55.810938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum201608
5-th percentile201711
Q1201905
median202009
Q3202110
95-th percentile202209
Maximum202212
Range604
Interquartile range (IQR)205

Descriptive statistics

Standard deviation151.89097
Coefficient of variation (CV)0.00075190731
Kurtosis-0.93025316
Mean202007.57
Median Absolute Deviation (MAD)102
Skewness-0.31734642
Sum1.40771 × 1010
Variance23070.867
MonotonicityNot monotonic
2025-05-27T18:52:56.077755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202006 1896
 
2.7%
202107 1693
 
2.4%
202111 1679
 
2.4%
202108 1627
 
2.3%
202205 1580
 
2.3%
202109 1543
 
2.2%
202208 1535
 
2.2%
201908 1528
 
2.2%
202105 1523
 
2.2%
201909 1516
 
2.2%
Other values (67) 53566
76.9%
ValueCountFrequency (%)
201608 3
 
< 0.1%
201609 22
 
< 0.1%
201610 35
 
0.1%
201611 81
 
0.1%
201612 103
0.1%
201701 74
 
0.1%
201702 102
0.1%
201703 159
0.2%
201704 137
0.2%
201705 238
0.3%
ValueCountFrequency (%)
202212 577
 
0.8%
202211 1353
1.9%
202210 956
1.4%
202209 1326
1.9%
202208 1535
2.2%
202207 1228
1.8%
202206 1373
2.0%
202205 1580
2.3%
202204 1256
1.8%
202203 1105
1.6%

periodo_publicacion
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.2 MiB

periodo_inicio_consultas
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.2 MiB

periodo_final_consultas
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.2 MiB
Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2025-05-27T18:52:56.596828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.7044457
Min length6

Characters and Unicode

Total characters536892
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201609
2nd row201609
3rd row201612
4th row201611
5th row201609
ValueCountFrequency (%)
sin 39592
36.2%
datos 39592
36.2%
202107 714
 
0.7%
202204 680
 
0.6%
202208 668
 
0.6%
201905 652
 
0.6%
201908 642
 
0.6%
202111 638
 
0.6%
202205 613
 
0.6%
202106 599
 
0.5%
Other values (69) 24888
22.8%
2025-05-27T18:52:57.277926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%
Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2025-05-27T18:52:57.725827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.7044457
Min length6

Characters and Unicode

Total characters536892
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201610
2nd row201609
3rd row201612
4th row201611
5th row201610
ValueCountFrequency (%)
sin 39592
36.2%
datos 39592
36.2%
202205 704
 
0.6%
201905 695
 
0.6%
202107 686
 
0.6%
202208 658
 
0.6%
201908 638
 
0.6%
202007 633
 
0.6%
202111 620
 
0.6%
202109 609
 
0.6%
Other values (69) 24851
22.7%
2025-05-27T18:52:58.853253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

periodo_acto_apertura
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.2 MiB

Interactions

2025-05-27T18:51:55.664048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:45.683714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:47.926195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:50.072690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:51.975101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:53.869983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:55.987504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:46.089399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:48.265337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:50.407824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:52.295794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:54.128550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:56.183863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:46.397901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:48.736018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:50.682791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:52.632956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:54.345220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:56.527629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:46.866265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:49.061722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:51.001316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:52.941271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:54.568172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:56.778753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:47.263553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:49.396741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:51.300924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:53.242604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:54.829515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:57.060175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:47.571312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:49.708606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:51.623181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:53.649413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-27T18:51:55.346876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-27T18:52:59.241822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Unnamed: 0.1acepta_prorrogaapartadoaño_aperturacotizacioncro_cant_dias_publicarencuadre_legalestadoetapaetapa_acto_aperturaetapa_autorizacion_llamadoetapa_autorizacion_pliegoetapa_licfinanciamiento_externogenera_recursosmodalidadmonedaofertas_confirmadasperiodo_aperturaproveedores_participantesrequiere_pagotipo_doc_compratipo_proceso
Unnamed: 0.11.0000.0360.0870.2320.0710.0220.1010.3430.0070.2030.1970.1990.0070.0680.0360.0420.0290.1750.2470.1000.0510.0310.096
acepta_prorroga0.0361.0000.1830.0450.0530.0000.1270.0140.0110.0460.0830.0820.0110.0000.0240.1140.0490.0670.0450.0170.0220.0780.075
apartado0.0870.1831.0000.1070.0940.0000.3440.1240.0120.9770.9580.9560.0120.0520.0850.0470.0510.0550.1000.0250.0360.1750.408
año_apertura0.2320.0450.1071.0000.0630.0420.1190.0490.0150.1470.1150.1160.0150.0570.0190.0610.0290.0330.982-0.0210.0250.0330.083
cotizacion0.0710.0530.0940.0631.0000.0120.0780.0000.0080.0020.0230.0230.0080.0190.0390.0520.0130.0320.0640.0000.0150.0480.063
cro_cant_dias_publicar0.0220.0000.0000.0420.0121.0000.0350.0000.0000.0310.0220.0220.0000.0000.0000.0140.0000.1310.0460.2110.0000.0000.037
encuadre_legal0.1010.1270.3440.1190.0780.0351.0000.0770.0550.9530.7260.7280.0551.0000.7940.3910.0650.0520.1100.0500.0510.5550.808
estado0.3430.0140.1240.0490.0000.0000.0771.0000.0080.0660.1050.1050.0080.0050.0440.0290.0320.0540.0490.0100.0000.0450.064
etapa0.0070.0110.0120.0150.0080.0000.0550.0081.0000.0270.0210.0211.0000.0000.0000.0090.0000.0000.0150.0000.0000.0000.122
etapa_acto_apertura0.2030.0460.9770.1470.0020.0310.9530.0660.0271.0000.7500.7500.0270.0540.0850.1470.0540.0790.1470.0350.0390.0810.975
etapa_autorizacion_llamado0.1970.0830.9580.1150.0230.0220.7260.1050.0210.7501.0000.9900.0210.0400.0670.0810.0650.0350.1150.0300.0290.1010.737
etapa_autorizacion_pliego0.1990.0820.9560.1160.0230.0220.7280.1050.0210.7500.9901.0000.0210.0410.0670.0810.0630.0360.1150.0300.0290.1000.738
etapa_lic0.0070.0110.0120.0150.0080.0000.0550.0081.0000.0270.0210.0211.0000.0000.0000.0090.0000.0000.0150.0000.0000.0000.122
financiamiento_externo0.0680.0000.0520.0570.0190.0001.0000.0050.0000.0540.0400.0410.0001.0000.0000.9180.0540.0000.0570.0030.0000.0090.405
genera_recursos0.0360.0240.0850.0190.0390.0000.7940.0440.0000.0850.0670.0670.0000.0001.0000.0660.0590.0690.0180.0730.0000.8460.791
modalidad0.0420.1140.0470.0610.0520.0140.3910.0290.0090.1470.0810.0810.0090.9180.0661.0000.0610.0100.0560.0000.0100.5800.101
moneda0.0290.0490.0510.0290.0130.0000.0650.0320.0000.0540.0650.0630.0000.0540.0590.0611.0000.0000.0270.0000.0000.0410.054
ofertas_confirmadas0.1750.0670.0550.0330.0320.1310.0520.0540.0000.0790.0350.0360.0000.0000.0690.0100.0001.0000.0240.9030.0000.0530.053
periodo_apertura0.2470.0450.1000.9820.0640.0460.1100.0490.0150.1470.1150.1150.0150.0570.0180.0560.0270.0241.000-0.0310.0250.0330.083
proveedores_participantes0.1000.0170.025-0.0210.0000.2110.0500.0100.0000.0350.0300.0300.0000.0030.0730.0000.0000.903-0.0311.0000.0000.0510.049
requiere_pago0.0510.0220.0360.0250.0150.0000.0510.0000.0000.0390.0290.0290.0000.0000.0000.0100.0000.0000.0250.0001.0000.0000.042
tipo_doc_compra0.0310.0780.1750.0330.0480.0000.5550.0450.0000.0810.1010.1000.0000.0090.8460.5800.0410.0530.0330.0510.0001.0000.553
tipo_proceso0.0960.0750.4080.0830.0630.0370.8080.0640.1220.9750.7370.7380.1220.4050.7910.1010.0540.0530.0830.0490.0420.5531.000

Missing values

2025-05-27T18:51:57.832936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-27T18:52:00.222982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

num_procesoExpedientenombre_procesotipo_procesofecha_aperturaestadounidad_ejecutoraservicio_administrativo_financieroUnnamed: 0.1num_expedienteetapamodalidadmonedaencuadre_legalcotizaciontipo_doc_compralugar_recepcionplazo_ofertarequiere_pagoapartadoetapa_licetapa_autorizacion_pliegoetapa_autorizacion_llamadoetapa_acto_aperturagenera_recursosfinanciamiento_externoacepta_prorrogacro_fecha_publicacioncro_fecha_inicio_consultascro_fecha_final_consultascro_cant_dias_publicarcro_fecha_inicio_recepcion_documentoscro_fecha_fin_recepcion_documentoscro_fecha_acto_aperturainicio_contratoduracion_contratoproveedores_participantesofertas_confirmadasaño_publicacionaño_aperturaperiodo_aperturaperiodo_publicacionperiodo_inicio_consultasperiodo_final_consultasperiodo_inicio_recepcion_documentosperiodo_fin_recepcion_documentosperiodo_acto_apertura
023-0009-LPR16EX-2016-00697885- -APN-DPYS#SGPAdquisición de elementos de electricidadLicitación Privada13/10/2016 13:02 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación0.0EX-2016-00697885- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo21/09/2016 18:01 Hrs.21/09/2016 18:02 Hrs.06/10/2016 13:01 Hrs.2.027/09/2016 18:02 Hrs.13/10/2016 13:02 Hrs.13/10/2016 13:02 Hrs.A partir del perfeccionamiento del documento contractual5 Días hábiles9.05.020162016201610201609201609201610201609201610201610
123-0010-LPR16EX-2016-01031683- -APN-DPYS#SGPAdquisición de elementos de plomería y cerrajería.Licitación Privada13/09/2016 11:00 Hrs.Desierto23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación1.0EX-2016-01031683- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25Se admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo02/09/2016 09:00 Hrs.02/09/2016 10:00 Hrs.07/09/2016 11:00 Hrs.1.002/09/2016 09:00 Hrs.13/09/2016 11:00 Hrs.13/09/2016 11:00 Hrs.Dentro de los 20 Días corridos del perfeccionamiento del documento contractual30 Días corridos2.00.020162016201609201609201609201609201609201609201609
223-0011-LPR16EX-2016-01358346- -APN-DDMYA#SGPADQUISICIÓN INSUMOS PARA BAÑOSLicitación Privada19/12/2016 12:30 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación2.0EX-2016-01358346- -APN-DDMYA#SGPÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo07/12/2016 18:30 Hrs.07/12/2016 19:00 Hrs.19/12/2016 12:00 Hrs.0.012/12/2016 10:00 Hrs.19/12/2016 12:00 Hrs.19/12/2016 12:30 Hrs.A partir del perfeccionamiento del documento contractual12 Meses8.03.020162016201612201612201612201612201612201612201612
323-0012-LPR16EX-2016-01392005- -APN-DDMYA#SGPServicio anual de mantenimiento, y controles mensuales de Extintores, y adquisiciónLicitación Privada30/11/2016 16:31 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación3.0EX-2016-01392005- -APN-DDMYA#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Perfeccionamiento del documento contractualNosin datosÚnicasin datossin datossin datosNoNoNo15/11/2016 16:30 Hrs.15/11/2016 16:31 Hrs.24/11/2016 16:31 Hrs.2.030/11/2016 16:30 Hrs.30/11/2016 16:30 Hrs.30/11/2016 16:31 Hrs.A partir del perfeccionamiento del documento contractual12 Meses6.05.020162016201611201611201611201611201611201611201611
423-0014-LPU16EX-2016-00474707- -APN-DPYS#SGPAdquisición de indumentaria.Licitación Pública27/10/2016 12:00 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación4.0EX-2016-00474707- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25Se admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo21/09/2016 15:00 Hrs.21/09/2016 15:01 Hrs.21/10/2016 12:00 Hrs.2.026/09/2016 15:01 Hrs.27/10/2016 11:59 Hrs.27/10/2016 12:00 Hrs.A partir del perfeccionamiento del documento contractual120 Días corridos7.06.020162016201610201609201609201610201609201610201610
523-0015-LPU16EX-2016-00549712- -APN-DPYS#SGPServicio de transporte aéreo para el Sr. Presidente y ComitivaLicitación Pública09/09/2016 12:01 Hrs.Dejado Sin Efecto23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación5.0EX-2016-00549712- -APN-DPYS#SGPÚnicaOrden de compra abiertaDolar EstadounidenseDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo09/08/2016 10:00 Hrs.09/08/2016 10:01 Hrs.06/09/2016 12:01 Hrs.2.009/08/2016 10:00 Hrs.09/09/2016 12:01 Hrs.09/09/2016 12:01 Hrs.A partir del perfeccionamiento del documento contractual12 Meses3.01.020162016201609201608201608201609201608201609201609
623-0017-LPU16EX-2016-00563975- -APN-DPYS#SGPServicio de mantenimiento integral, correctivo y preventivo de espacios verdes para la RPO.Licitación Pública14/11/2016 11:00 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación6.0EX-2016-00563975- -APN-DPYS#SGPÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días hábiles Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo05/09/2016 13:00 Hrs.05/09/2016 13:30 Hrs.08/11/2016 10:00 Hrs.2.019/10/2016 14:00 Hrs.14/11/2016 11:00 Hrs.14/11/2016 11:00 Hrs.A partir del perfeccionamiento del documento contractual12 Meses13.08.020162016201611201609201609201611201610201611201611
723-0018-LPU16EX-2016-00669730- -APN-DPYS#SGPServicio de Limpieza Integral destinado a distintos edificios dependientes de la SECRETARIA GENERAL.Licitación Pública07/11/2016 12:00 Hrs.Dejado Sin Efecto23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación7.0EX-2016-00669730- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNo04/10/2016 13:00 Hrs.06/10/2016 13:00 Hrs.01/11/2016 12:00 Hrs.2.006/10/2016 09:00 Hrs.07/11/2016 12:00 Hrs.07/11/2016 12:00 Hrs.A partir del perfeccionamiento del documento contractual12 Meses11.06.020162016201611201610201610201611201610201611201611
823-0019-LPU16EX-2016-00670612- -APN-DPYS#SGPServicio de mantenimiento de Aires Acondicionados y otros equipos para dependencias de la SEC. GRAL.Licitación Pública13/10/2016 11:05 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación8.0EX-2016-00670612- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo02/09/2016 08:00 Hrs.02/09/2016 08:01 Hrs.07/10/2016 17:00 Hrs.2.014/09/2016 08:01 Hrs.13/10/2016 11:05 Hrs.13/10/2016 11:05 Hrs.A partir del perfeccionamiento del documento contractual12 Meses8.04.020162016201610201609201609201610201609201610201610
923-0020-LPU16EX-2016-00801371- -APN-DPYS#SGPProvisión e Instalación de un Sistema de Iluminación Exterior del Edificio de Casa de GobiernoLicitación Pública21/10/2016 11:05 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la Nación9.0EX-2016-00801371- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNosin datosÚnicasin datossin datossin datosNoNoNo19/09/2016 12:00 Hrs.19/09/2016 12:05 Hrs.18/10/2016 11:05 Hrs.2.022/09/2016 12:00 Hrs.21/10/2016 11:05 Hrs.21/10/2016 11:05 Hrs.A partir del perfeccionamiento del documento contractual90 Días corridos8.02.020162016201610201609201609201610201609201610201610
num_procesoExpedientenombre_procesotipo_procesofecha_aperturaestadounidad_ejecutoraservicio_administrativo_financieroUnnamed: 0.1num_expedienteetapamodalidadmonedaencuadre_legalcotizaciontipo_doc_compralugar_recepcionplazo_ofertarequiere_pagoapartadoetapa_licetapa_autorizacion_pliegoetapa_autorizacion_llamadoetapa_acto_aperturagenera_recursosfinanciamiento_externoacepta_prorrogacro_fecha_publicacioncro_fecha_inicio_consultascro_fecha_final_consultascro_cant_dias_publicarcro_fecha_inicio_recepcion_documentoscro_fecha_fin_recepcion_documentoscro_fecha_acto_aperturainicio_contratoduracion_contratoproveedores_participantesofertas_confirmadasaño_publicacionaño_aperturaperiodo_aperturaperiodo_publicacionperiodo_inicio_consultasperiodo_final_consultasperiodo_inicio_recepcion_documentosperiodo_fin_recepcion_documentosperiodo_acto_apertura
6967695-0021-CDI22EX-2022-33725983- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NACIONAL PARA EL RELEVAMIENTO DE POLITICAS DEL MINIST.Contratación Directa20/04/2022 10:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo Social20.0EX-2022-33725983- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de comprasin datos90 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicasin datossin datosActo de aperturaNoNoNo12/04/2022 15:00 Hrs.12/04/2022 15:01 Hrs.18/04/2022 10:00 Hrs.0.0sin datossin datos20/04/2022 10:00 Hrs.Dentro de los 45 Días corridos del perfeccionamiento del documento contractual12 Meses0.00.020222022202204202204202204202204sin datossin datos202204
6967795-0038-CDI22EX-2022-67241200- -APN-DCYC#MDSservicio de provisión de combustibles y lubricantesContratación Directa17/08/2022 08:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo Social35.0EX-2022-67241200- -APN-DCYC#MDSÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14Se admite cotización parcial por renglónOrden de comprasin datos60 Días corridos Acto de aperturaNoApartado 8: Adjudicación Simple InteradministrativaÚnicasin datossin datosActo de aperturaNoNoNo09/08/2022 20:00 Hrs.09/08/2022 20:01 Hrs.16/08/2022 08:00 Hrs.0.0sin datossin datos17/08/2022 08:00 Hrs.Dentro de los 5 Días corridos del perfeccionamiento del documento contractual12 Meses0.00.020222022202208202208202208202208sin datossin datos202208
6967895-0049-CDI22EX-2022-82329158- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NACIONAL PARA REALIZAR UNA ACTUALIZACIÓN CARTOGRÁFICAContratación Directa06/09/2022 10:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo Social45.0EX-2022-82329158- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de comprasin datos60 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicasin datossin datosActo de aperturaNoNoNo26/08/2022 18:30 Hrs.26/08/2022 18:31 Hrs.31/08/2022 10:00 Hrs.0.0sin datossin datos06/09/2022 10:00 Hrs.Dentro de los 5 Días hábiles del perfeccionamiento del documento contractual12 Meses0.00.020222022202209202208202208202208sin datossin datos202209
6967995-0065-CDI22EX-2022-103379128- -APN-DCYC#MDSservicio de provisión de combustibles y lubricantesContratación Directa11/10/2022 09:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo Social31.0EX-2022-103379128- -APN-DCYC#MDSÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de comprasin datos60 Días corridos Acto de aperturaNoApartado 8: Adjudicación Simple InteradministrativaÚnicasin datossin datosActo de aperturaNoNoNo04/10/2022 11:00 Hrs.04/10/2022 11:01 Hrs.06/10/2022 09:00 Hrs.0.0sin datossin datos11/10/2022 09:00 Hrs.Dentro de los 5 Días corridos del perfeccionamiento del documento contractual12 Meses0.00.020222022202210202210202210202210sin datossin datos202210
6968095-0069-CDI22EX-2022-105655311- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NAC. PARA LA REALIZACIÓN DE UNA DIPLOMATURAContratación Directa31/10/2022 09:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo Social32.0EX-2022-105655311- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de comprasin datos60 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicasin datossin datosActo de aperturaNoNoNo24/10/2022 19:40 Hrs.24/10/2022 19:41 Hrs.27/10/2022 09:00 Hrs.0.0sin datossin datos31/10/2022 09:00 Hrs.A partir del perfeccionamiento del documento contractual10 Meses0.00.020222022202210202210202210202210sin datossin datos202210
6968195-0189-CDI21EX-2021-124397120- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NACIONAL PARA EL RELEVAMIENTO DE POLITICAS SOCIALESContratación Directa18/01/2022 10:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo Social12.0EX-2021-124397120- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de comprasin datos90 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicasin datossin datosActo de aperturaNoNoNo11/01/2022 20:00 Hrs.11/01/2022 20:01 Hrs.14/01/2022 10:00 Hrs.0.0sin datossin datos18/01/2022 10:00 Hrs.Dentro de los 10 Días corridos del perfeccionamiento del documento contractual7 Meses0.00.020222022202201202201202201202201sin datossin datos202201
6968296-0054-CDI22EX-2022-44338308- -APN-DC#HPADQUISICIÓN DE ÁCIDO 2,3 - DIMERCAPTOSUCCÍNICO PARA EL SERVICIO DE TOXICOLOGÍA.Contratación Directa21/06/2022 13:00 Hrs.Desierto96 - Dirección General de Administración908 - Hospital Nacional Profesor Alejandro Posadas33.0EX-2022-44338308- -APN-DC#HPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14Se admite cotización parcial por renglónOrden de compraPTE. ILLIA y MARCONI C.P. (1684) PALOMAR60 Días corridos Acto de aperturaNoApartado 1: Compulsa Abreviada Por MontoÚnicaAutorización del pliegoAutorización de llamadoActo de aperturaNoNoNo13/06/2022 08:00 Hrs.13/06/2022 09:00 Hrs.14/06/2022 13:00 Hrs.0.0sin datossin datos21/06/2022 13:00 Hrs.A partir del perfeccionamiento del documento contractual6 Meses0.00.020222022202206202206202206202206sin datossin datos202206
6968396-0118-CDI22EX-2022-121335498- -APN-DC#HPCALIBRACIÓN DE EQUIPAMIENTO Y ELEMENTOS DE MEDICIÓNContratación Directa23/11/2022 13:00 Hrs.Desierto96 - Dirección General de Administración908 - Hospital Nacional Profesor Alejandro Posadas42.0EX-2022-121335498- -APN-DC#HPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14Se admite cotización parcial por renglónOrden de compraPTE. ILLIA y MARCONI C.P. (1684) PALOMAR60 Días corridos Acto de aperturaNoApartado 1: Compulsa Abreviada Por MontoÚnicaAutorización del pliegoAutorización de llamadoActo de aperturaNoNoNo17/11/2022 08:00 Hrs.17/11/2022 09:00 Hrs.22/11/2022 13:00 Hrs.0.0sin datossin datos23/11/2022 13:00 Hrs.A partir del perfeccionamiento del documento contractual10 Días hábiles0.00.020222022202211202211202211202211sin datossin datos202211
6968498-0012-CDI22EX-2022-44810023- -APN-DA#FMLCAVADQUISICIÓN DE VACUNAS ANTIGRIPALESContratación Directa16/05/2022 11:00 Hrs.Desierto98/00 - Dir. Compras101 - Fundación Miguel Lillo36.0EX-2022-44810023- -APN-DA#FMLCAVÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25Se admite cotización parcial por renglónContratoMiguel Lillo N° 251, S.M. de Tucumán, Tucumán30 Días corridos Acto de aperturaNoApartado 8: Adjudicación Simple InteradministrativaÚnicaAutorización del pliegoAutorización de llamadosin datosNoNoNo16/05/2022 08:30 Hrs.16/05/2022 09:00 Hrs.16/05/2022 10:00 Hrs.0.0sin datossin datos16/05/2022 11:00 Hrs.A partir del perfeccionamiento del documento contractual6 Meses0.00.020222022202205202205202205202205sin datossin datos202205
6968598-0027-CDI22EX-2022-108159624- -APN-DA#FMLCAVSERVICIO DE CAMBIO DE ALFOMBRA EN EL CENTRO CULTURAL.-Contratación Directa04/11/2022 11:00 Hrs.Desierto98/00 - Dir. Compras101 - Fundación Miguel Lillo43.0EX-2022-108159624- -APN-DA#FMLCAVÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraMiguel Lillo N° 251, S.M. de Tucumán, Tucumán30 Días corridos Acto de aperturaNoApartado 1: Compulsa Abreviada Por MontoÚnicaAutorización del pliegoAutorización de llamadoActo de aperturaNoNoNo19/10/2022 13:00 Hrs.19/10/2022 16:00 Hrs.28/10/2022 09:00 Hrs.0.0sin datossin datos04/11/2022 11:00 Hrs.A partir del perfeccionamiento del documento contractual30 Días hábiles0.00.020222022202211202210202210202210sin datossin datos202211